mirror of
https://github.com/kvcache-ai/ktransformers.git
synced 2025-09-05 20:19:51 +00:00
57 lines
1.8 KiB
Python
57 lines
1.8 KiB
Python
import os
|
|
os.environ["CUDA_VISIBLE_DEVICES"]="1"
|
|
# add path
|
|
import sys
|
|
current_path = os.path.abspath(os.path.dirname(__file__))
|
|
sys.path.append(current_path+"/../..")
|
|
import pycuda.autoinit
|
|
import pycuda.driver as cuda
|
|
from pycuda.compiler import SourceModule
|
|
import numpy as np
|
|
# from ktransformers.operators.linear import KTransformerLinear, QuantizedLinearMarlin
|
|
# from ktransformers.operators.experts import KTransformersMLPExpert, MLPExpertsTorch
|
|
from ktransformers.util.custom_gguf import GGUFLoader
|
|
import torch
|
|
import KTransformersOps
|
|
torch.set_default_dtype(torch.bfloat16)
|
|
import time
|
|
from transformers import (
|
|
AutoConfig,
|
|
)
|
|
|
|
gguf_config = GGUFLoader("/data/Qwen2-57B-A14B-Instruct-GGUF/q4_k_m")
|
|
model_name = "/data/Qwen2-57B-A14B-Instruct"
|
|
key = "blk.0."
|
|
target = "ffn_down_exps.weight"
|
|
|
|
t1 = time.time()
|
|
q_weight_cpu = gguf_config.load_gguf_tensor(key+target, "cpu")
|
|
# q_weight_cpu = torch.from_numpy(q_weight_cpu)
|
|
|
|
t2 = time.time()
|
|
q_weight_gpu = gguf_config.load_gguf_tensor(key+target, "cuda")
|
|
t3 = time.time()
|
|
print()
|
|
allclose = torch.allclose(q_weight_cpu, q_weight_gpu.cpu().to(torch.float32), atol=1e-6)
|
|
print(f"Q6k {key+target}")
|
|
print("load gguf tensor from cpu cost: ", t2-t1)
|
|
print("load gguf tensor from gpu cost: ", t3-t2)
|
|
print("allclose: ", allclose)
|
|
|
|
|
|
key = "blk.1."
|
|
target = "ffn_up_shexp.weight"
|
|
|
|
t1 = time.time()
|
|
q_weight_cpu = gguf_config.load_gguf_tensor(key+target, "cpu")
|
|
# q_weight_cpu = torch.from_numpy(q_weight_cpu)
|
|
|
|
t2 = time.time()
|
|
q_weight_gpu = gguf_config.load_gguf_tensor(key+target, "cuda")
|
|
t3 = time.time()
|
|
print()
|
|
allclose = torch.allclose(q_weight_cpu, q_weight_gpu.cpu(), atol=1e-6)
|
|
print(f"Q4k {key+target}")
|
|
print("load gguf tensor from cpu cost: ", t2-t1)
|
|
print("load gguf tensor from gpu cost: ", t3-t2)
|
|
print("allclose: ", allclose)
|